Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104889
Title: Robotics and Computer-Integrated Manufacturing
In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning
Authors: Pandiyan, Vigneashwara
Murugan, Pushparaja
Tjahjowidodo, Tegoeh
Caesarendra, Wahyu
Manyar, Omey Mohan
Then, David Jin Hong
Keywords: Deep Learning
Abrasive Belt Grinding
DRNTU::Engineering::Mechanical engineering
Issue Date: 2019
Source: Pandiyan, V., Murugan, P., Tjahjowidodo, T., Caesarendra, W., Manyar, O. M., & Then, D. J. H. (2019). In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robotics and Computer-Integrated Manufacturing, 57477-487. doi:10.1016/j.rcim.2019.01.006
Series/Report no.: Robotics and Computer-Integrated Manufacturing
Abstract: Transforming the manufacturing environment from manually operated production units to unsupervised robotic machining centres requires a presence of reliable in-process monitoring system. In this paper, we demonstrate a technique for automatic endpoint detection of weld seam removal in a robotic abrasive belt grinding process with the help of a vision system using deep learning. The paper presents the results of the first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN). An experimental investigation using four different weld seam states on mild steel work coupon are trained using the VGG-16 network based on encoder-decoder architecture. The results demonstrate the potential of the developed vision based methodology as a tool for endpoint prediction of the weld seam removal in real time during a compliant abrasive belt grinding process. The prediction system based on semantic segmentation is able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which will allow further process optimisation.
URI: https://hdl.handle.net/10356/104889
http://hdl.handle.net/10220/48057
ISSN: 0736-5845
DOI: 10.1016/j.rcim.2019.01.006
Rights: © 2019 Elsevier. All rights reserved. This paper was published in Robotics and Computer-Integrated Manufacturing and is made available with permission of Elsevier.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.